Cargando…

Neuroimaging of tissue microstructure as a marker of neurodegeneration in the AT(N) framework: defining abnormal neurodegeneration and improving prediction of clinical status

BACKGROUND: Alzheimer’s disease involves accumulating amyloid (A) and tau (T) pathology, and progressive neurodegeneration (N), leading to the development of the AD clinical syndrome. While several markers of N have been proposed, efforts to define normal vs. abnormal neurodegeneration based on neur...

Descripción completa

Detalles Bibliográficos
Autores principales: Gallagher, Rigina L., Koscik, Rebecca Langhough, Moody, Jason F., Vogt, Nicholas M., Adluru, Nagesh, Kecskemeti, Steven R., Van Hulle, Carol A., Chin, Nathaniel A., Asthana, Sanjay, Kollmorgen, Gwendlyn, Suridjan, Ivonne, Carlsson, Cynthia M., Johnson, Sterling C., Dean, Douglas C., Zetterberg, Henrik, Blennow, Kaj, Alexander, Andrew L., Bendlin, Barbara B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10583332/
https://www.ncbi.nlm.nih.gov/pubmed/37848950
http://dx.doi.org/10.1186/s13195-023-01281-y
_version_ 1785122527498993664
author Gallagher, Rigina L.
Koscik, Rebecca Langhough
Moody, Jason F.
Vogt, Nicholas M.
Adluru, Nagesh
Kecskemeti, Steven R.
Van Hulle, Carol A.
Chin, Nathaniel A.
Asthana, Sanjay
Kollmorgen, Gwendlyn
Suridjan, Ivonne
Carlsson, Cynthia M.
Johnson, Sterling C.
Dean, Douglas C.
Zetterberg, Henrik
Blennow, Kaj
Alexander, Andrew L.
Bendlin, Barbara B.
author_facet Gallagher, Rigina L.
Koscik, Rebecca Langhough
Moody, Jason F.
Vogt, Nicholas M.
Adluru, Nagesh
Kecskemeti, Steven R.
Van Hulle, Carol A.
Chin, Nathaniel A.
Asthana, Sanjay
Kollmorgen, Gwendlyn
Suridjan, Ivonne
Carlsson, Cynthia M.
Johnson, Sterling C.
Dean, Douglas C.
Zetterberg, Henrik
Blennow, Kaj
Alexander, Andrew L.
Bendlin, Barbara B.
author_sort Gallagher, Rigina L.
collection PubMed
description BACKGROUND: Alzheimer’s disease involves accumulating amyloid (A) and tau (T) pathology, and progressive neurodegeneration (N), leading to the development of the AD clinical syndrome. While several markers of N have been proposed, efforts to define normal vs. abnormal neurodegeneration based on neuroimaging have been limited. Sensitive markers that may account for or predict cognitive dysfunction for individuals in early disease stages are critical. METHODS: Participants (n = 296) defined on A and T status and spanning the AD-clinical continuum underwent multi-shell diffusion-weighted magnetic resonance imaging to generate Neurite Orientation Dispersion and Density Imaging (NODDI) metrics, which were tested as markers of N. To better define N, we developed age- and sex-adjusted robust z-score values to quantify normal and AD-associated (abnormal) neurodegeneration in both cortical gray matter and subcortical white matter regions of interest. We used general logistic regression with receiver operating characteristic (ROC) and area under the curve (AUC) analysis to test whether NODDI metrics improved diagnostic accuracy compared to models that only relied on cerebrospinal fluid (CSF) A and T status (alone and in combination). RESULTS: Using internal robust norms, we found that NODDI metrics correlate with worsening cognitive status and that NODDI captures early, AD neurodegenerative pathology in the gray matter of cognitively unimpaired, but A/T biomarker-positive, individuals. NODDI metrics utilized together with A and T status improved diagnostic prediction accuracy of AD clinical status, compared with models using CSF A and T status alone. CONCLUSION: Using a robust norms approach, we show that abnormal AD-related neurodegeneration can be detected among cognitively unimpaired individuals. Metrics derived from diffusion-weighted imaging are potential sensitive markers of N and could be considered for trial enrichment and as outcomes in clinical trials. However, given the small sample sizes, the exploratory nature of the work must be acknowledged. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13195-023-01281-y.
format Online
Article
Text
id pubmed-10583332
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-105833322023-10-19 Neuroimaging of tissue microstructure as a marker of neurodegeneration in the AT(N) framework: defining abnormal neurodegeneration and improving prediction of clinical status Gallagher, Rigina L. Koscik, Rebecca Langhough Moody, Jason F. Vogt, Nicholas M. Adluru, Nagesh Kecskemeti, Steven R. Van Hulle, Carol A. Chin, Nathaniel A. Asthana, Sanjay Kollmorgen, Gwendlyn Suridjan, Ivonne Carlsson, Cynthia M. Johnson, Sterling C. Dean, Douglas C. Zetterberg, Henrik Blennow, Kaj Alexander, Andrew L. Bendlin, Barbara B. Alzheimers Res Ther Research BACKGROUND: Alzheimer’s disease involves accumulating amyloid (A) and tau (T) pathology, and progressive neurodegeneration (N), leading to the development of the AD clinical syndrome. While several markers of N have been proposed, efforts to define normal vs. abnormal neurodegeneration based on neuroimaging have been limited. Sensitive markers that may account for or predict cognitive dysfunction for individuals in early disease stages are critical. METHODS: Participants (n = 296) defined on A and T status and spanning the AD-clinical continuum underwent multi-shell diffusion-weighted magnetic resonance imaging to generate Neurite Orientation Dispersion and Density Imaging (NODDI) metrics, which were tested as markers of N. To better define N, we developed age- and sex-adjusted robust z-score values to quantify normal and AD-associated (abnormal) neurodegeneration in both cortical gray matter and subcortical white matter regions of interest. We used general logistic regression with receiver operating characteristic (ROC) and area under the curve (AUC) analysis to test whether NODDI metrics improved diagnostic accuracy compared to models that only relied on cerebrospinal fluid (CSF) A and T status (alone and in combination). RESULTS: Using internal robust norms, we found that NODDI metrics correlate with worsening cognitive status and that NODDI captures early, AD neurodegenerative pathology in the gray matter of cognitively unimpaired, but A/T biomarker-positive, individuals. NODDI metrics utilized together with A and T status improved diagnostic prediction accuracy of AD clinical status, compared with models using CSF A and T status alone. CONCLUSION: Using a robust norms approach, we show that abnormal AD-related neurodegeneration can be detected among cognitively unimpaired individuals. Metrics derived from diffusion-weighted imaging are potential sensitive markers of N and could be considered for trial enrichment and as outcomes in clinical trials. However, given the small sample sizes, the exploratory nature of the work must be acknowledged. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13195-023-01281-y. BioMed Central 2023-10-17 /pmc/articles/PMC10583332/ /pubmed/37848950 http://dx.doi.org/10.1186/s13195-023-01281-y Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Gallagher, Rigina L.
Koscik, Rebecca Langhough
Moody, Jason F.
Vogt, Nicholas M.
Adluru, Nagesh
Kecskemeti, Steven R.
Van Hulle, Carol A.
Chin, Nathaniel A.
Asthana, Sanjay
Kollmorgen, Gwendlyn
Suridjan, Ivonne
Carlsson, Cynthia M.
Johnson, Sterling C.
Dean, Douglas C.
Zetterberg, Henrik
Blennow, Kaj
Alexander, Andrew L.
Bendlin, Barbara B.
Neuroimaging of tissue microstructure as a marker of neurodegeneration in the AT(N) framework: defining abnormal neurodegeneration and improving prediction of clinical status
title Neuroimaging of tissue microstructure as a marker of neurodegeneration in the AT(N) framework: defining abnormal neurodegeneration and improving prediction of clinical status
title_full Neuroimaging of tissue microstructure as a marker of neurodegeneration in the AT(N) framework: defining abnormal neurodegeneration and improving prediction of clinical status
title_fullStr Neuroimaging of tissue microstructure as a marker of neurodegeneration in the AT(N) framework: defining abnormal neurodegeneration and improving prediction of clinical status
title_full_unstemmed Neuroimaging of tissue microstructure as a marker of neurodegeneration in the AT(N) framework: defining abnormal neurodegeneration and improving prediction of clinical status
title_short Neuroimaging of tissue microstructure as a marker of neurodegeneration in the AT(N) framework: defining abnormal neurodegeneration and improving prediction of clinical status
title_sort neuroimaging of tissue microstructure as a marker of neurodegeneration in the at(n) framework: defining abnormal neurodegeneration and improving prediction of clinical status
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10583332/
https://www.ncbi.nlm.nih.gov/pubmed/37848950
http://dx.doi.org/10.1186/s13195-023-01281-y
work_keys_str_mv AT gallagherriginal neuroimagingoftissuemicrostructureasamarkerofneurodegenerationintheatnframeworkdefiningabnormalneurodegenerationandimprovingpredictionofclinicalstatus
AT koscikrebeccalanghough neuroimagingoftissuemicrostructureasamarkerofneurodegenerationintheatnframeworkdefiningabnormalneurodegenerationandimprovingpredictionofclinicalstatus
AT moodyjasonf neuroimagingoftissuemicrostructureasamarkerofneurodegenerationintheatnframeworkdefiningabnormalneurodegenerationandimprovingpredictionofclinicalstatus
AT vogtnicholasm neuroimagingoftissuemicrostructureasamarkerofneurodegenerationintheatnframeworkdefiningabnormalneurodegenerationandimprovingpredictionofclinicalstatus
AT adlurunagesh neuroimagingoftissuemicrostructureasamarkerofneurodegenerationintheatnframeworkdefiningabnormalneurodegenerationandimprovingpredictionofclinicalstatus
AT kecskemetistevenr neuroimagingoftissuemicrostructureasamarkerofneurodegenerationintheatnframeworkdefiningabnormalneurodegenerationandimprovingpredictionofclinicalstatus
AT vanhullecarola neuroimagingoftissuemicrostructureasamarkerofneurodegenerationintheatnframeworkdefiningabnormalneurodegenerationandimprovingpredictionofclinicalstatus
AT chinnathaniela neuroimagingoftissuemicrostructureasamarkerofneurodegenerationintheatnframeworkdefiningabnormalneurodegenerationandimprovingpredictionofclinicalstatus
AT asthanasanjay neuroimagingoftissuemicrostructureasamarkerofneurodegenerationintheatnframeworkdefiningabnormalneurodegenerationandimprovingpredictionofclinicalstatus
AT kollmorgengwendlyn neuroimagingoftissuemicrostructureasamarkerofneurodegenerationintheatnframeworkdefiningabnormalneurodegenerationandimprovingpredictionofclinicalstatus
AT suridjanivonne neuroimagingoftissuemicrostructureasamarkerofneurodegenerationintheatnframeworkdefiningabnormalneurodegenerationandimprovingpredictionofclinicalstatus
AT carlssoncynthiam neuroimagingoftissuemicrostructureasamarkerofneurodegenerationintheatnframeworkdefiningabnormalneurodegenerationandimprovingpredictionofclinicalstatus
AT johnsonsterlingc neuroimagingoftissuemicrostructureasamarkerofneurodegenerationintheatnframeworkdefiningabnormalneurodegenerationandimprovingpredictionofclinicalstatus
AT deandouglasc neuroimagingoftissuemicrostructureasamarkerofneurodegenerationintheatnframeworkdefiningabnormalneurodegenerationandimprovingpredictionofclinicalstatus
AT zetterberghenrik neuroimagingoftissuemicrostructureasamarkerofneurodegenerationintheatnframeworkdefiningabnormalneurodegenerationandimprovingpredictionofclinicalstatus
AT blennowkaj neuroimagingoftissuemicrostructureasamarkerofneurodegenerationintheatnframeworkdefiningabnormalneurodegenerationandimprovingpredictionofclinicalstatus
AT alexanderandrewl neuroimagingoftissuemicrostructureasamarkerofneurodegenerationintheatnframeworkdefiningabnormalneurodegenerationandimprovingpredictionofclinicalstatus
AT bendlinbarbarab neuroimagingoftissuemicrostructureasamarkerofneurodegenerationintheatnframeworkdefiningabnormalneurodegenerationandimprovingpredictionofclinicalstatus